State-of-the-art video coding standards, such as H.264 achieve high compression efficiency based on two important assumptions. First, the encoded data is assumed to come from spatially and temporally sampled natural (real world) scenes. Such signals are mostly continuous in space and, due to relatively high frame rates, the change of information from one frame to the next is usually small—it is mostly due to camera or object motion. Video encoders exploit this spatio-temporal redundancy by only encoding data that cannot be derived or predicted from spatial or temporal neighborhoods.
Unfortunately, compression tools that are designed with the above assumptions in mind are sub-optimal for depth coding. Their use, even in high-bandwidth scenarios, can lead to subpar three-dimensional (3D) video quality. Approaches specific to computer-graphics applications can be prohibitively expensive in terms of bandwidth requirements. Thus, there is a need for improving coding of depth data to improve compression rates and reduce the bandwidth needed to transmit the depth data.